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        <title>Learning Invariant Representations of Social Media Users</title>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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    <abstract>The evolution of social media users’ behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naive approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features. We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content. Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1178</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Learning Invariant Representations of Social Media Users
%A Andrews, Nicholas
%A Bishop, Marcus
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F andrews-bishop-2019-learning
%X The evolution of social media users’ behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naive approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features. We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content. Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.
%R 10.18653/v1/D19-1178
%U https://aclanthology.org/D19-1178
%U https://doi.org/10.18653/v1/D19-1178
%P 1684-1695

Markdown (Informal)

[Learning Invariant Representations of Social Media Users](https://aclanthology.org/D19-1178) (Andrews & Bishop, EMNLP-IJCNLP 2019)

• Learning Invariant Representations of Social Media Users (Andrews & Bishop, EMNLP-IJCNLP 2019)

ACL

• Nicholas Andrews and Marcus Bishop. 2019. Learning Invariant Representations of Social Media Users. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1684–1695, Hong Kong, China. Association for Computational Linguistics.